title: Boosting specificity of MEG artifact removal by weighted support vector machine.
abstract: An automatic artifact removal method of magnetoencephalogram (MEG) was presented in this paper. The method proposed is based on independent components analysis (ICA) and support vector machine (SVM). However, different from the previous studies, in this paper we consider two factors which would influence the performance. First, the imbalance factor of independent components (ICs) of MEG is handled by weighted SVM. Second, instead of simply setting a fixed weight to each class, a re-weighting scheme is used for the preservation of useful MEG ICs. Experimental results on manually marked MEG dataset showed that the method proposed could correctly distinguish the artifacts from the MEG ICs. Meanwhile, 99.72% ± 0.67 of MEG ICs were preserved. The classification accuracy was 97.91% ± 1.39. In addition, it was found that this method was not sensitive to individual differences. The cross validation (leave-one-subject-out) results showed an averaged accuracy of 97.41% ± 2.14.
authors.forename: Fang
authors.initials: F
authors.lastname: Duan
authors.forename: Montri
authors.initials: M
authors.lastname: Phothisonothai
authors.forename: Mitsuru
authors.initials: M
authors.lastname: Kikuchi
authors.forename: Yuko
authors.initials: Y
authors.lastname: Yoshimura
authors.forename: Yoshio
authors.initials: Y
authors.lastname: Minabe
authors.forename: Kastumi
authors.initials: K
authors.lastname: Watanabe
authors.forename: Kazuyuki
authors.initials: K
authors.lastname: Aihara
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000473:pathology; D017709:Fractals; D006801:Humans; D015225:Magnetoencephalography; D016011:Normal Distribution; D011336:Probability; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine